TY  - JOUR
T1  - Investigation of Optimal Segmentation Algorithm for CT Lung
Nodules Using Cad System
AU - Sakthivel, K. AU - Balu, S. AU - Babu, C. Nelson Kennedy AU - Balamurugan, R. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 19
SP  - 3742
EP  - 3747
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.3742.3747
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.3742.3747
KW  - Segmentation
KW  -computer aided diagnosis
KW  -region growing
KW  -earth movers distance
KW  -firefly search
KW  -fuzzy c-means
AB  - Computer Aided Diagnosis (CAD) has been playing a significant role in cancer detection for the past two decades. This research study mainly focuses on developing a CAD system for early detection of lung cancer with improved accuracy. The proposed system helps to reduce unnecessary biopsy and surgery. In this research, three methodologies namely Automatic Region Growing (ARG), Histogram based Earth-Mover’s Distance (HEMD), Firefly Search Fuzzy C-Means (FSFC) algorithm have been developed for improving the accuracy of the computer aided diagnosis of lung cancer from CT images. The performance of three segmentation methodologies are evaluated and compared to prove that the proposed Firefly Search Fuzzy C-means methodology outperforms the existing algorithms.
ER  -